Data Science Grand Challenges
Data Science Grand Challenges
Carl Boettiger
Data Science Grand Challenges
Data Science Grand Challenges
- Inference vs prediction
- Dealing with assumptions
- Quantifying uncertainty
- Reproducibility
- Keeping the Science in Data Science
- Keeping the Scientists in data science: Career questions
Inference vs prediction
Prediction:

Inference vs Prediction

Dealing with assumptions
- Keeping assumptions in focus during analysis
- Communicating assumptions to audience
- Testing assumptions
Quantifying uncertainty
- formal uncertainty vs acutal uncertainty
- uncertainty with respect to assumptions
- communicating uncertainty
Reproducibility
- Data Science vs Open Science
Keeping the Science in Data Science

Keeping the Scientist in Data Science
Academic career paths for data scientists?
- Departmental (or interdepartmental) homes
- the “Researcher-Developer”: recognition for data and software products?
- the path of the “Research Software Engineer”